elastic_change_fpca: Elastic Changepoint Detection

In fdasrvf: Elastic Functional Data Analysis

Elastic Changepoint Detection

Description

This function identifies changepoints using a functional PCA

Usage

elastic_change_fpca(
  f,
  time,
  pca.method = "combined",
  pc = 0.95,
  d = 1000,
  n_pcs = 5,
  smooth_data = FALSE,
  sparam = 25,
  showplot = TRUE
)

Arguments

f	matrix (N x M) of M functions with N samples
time	vector of size N describing the sample points
pca.method	string specifying pca method (options = "combined", "vert", or "horiz", default = "combined")
pc	percentage of cummulation explained variance (default = 0.95)
d	number of monte carlo iterations of Brownian Bridge (default = 1000)
n_pcs	scalar specify number of principal components (default = 5)
smooth_data	smooth data using box filter (default = F)
sparam	number of times to apply box filter (default = 25)
showplot	show results plots (default = T)

Value

Returns a list object containing

pvalue	p value
change	indice of changepoint
DataBefore	functions before changepoint
DataAfter	functions after changepoint
MeanBefore	mean function before changepoint
MeanAfter	mean function after changepoint
WarpingBefore	warping functions before changepoint
WarpingAfter	warping functions after changepoint
WarpingMeanBefore	mean warping function before changepoint
WarpingMeanAfter	mean warping function after changepoint
change_fun	amplitude change function
Sn	test statistic values

References

J. D. Tucker and D. Yarger, “Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources”, Envirometrics, 10.1002/env.2826, 2023.

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.